Author(s): Nikhil

Originally published on Towards AI.

When you’re building or deploying a large language model (LLM), one critical question emerges: how do you know if it’s actually good?

Unlike traditional software where you can measure success with clear metrics like “did it crash?”, evaluating LLMs is more nuanced. You need to assess whether the model generates accurate, coherent, and relevant responses. That’s where evaluations or “evals” come in.

The article discusses the evaluation of large language models (LLMs), emphasizing the need for systematic assessments called “evals” to determine their effectiveness, ethics, and safety. It describes three main approaches: automated metrics, human evaluations, and using another LLM as a judge. Each method has its advantages and challenges, and a combination of all three is considered the best strategy for accurate evaluation. The article also highlights popular benchmarks that are utilized for standardized comparisons.

Read the full blog for free on Medium.

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Published via Towards AI